Package ‘bmk’ July 2, 2014 Type Package Title MCMC diagnostics package Version 1.0 Date[removed]

Source URL: cran.r-project.org

• Hellinger distance
• Probability theory
• Estimator
• Statistics
• Estimation theory
• Statistical inference

PRECONDITIONING MARKOV CHAIN MONTE CARLO SIMULATIONS USING COARSE-SCALE MODELS Y. EFENDIEV∗ , T. HOU† , AND W. LUO‡ Abstract. We study the preconditioning of Markov Chain Monte Carlo (MCMC) methods using coarse-sca

Source URL: users.cms.caltech.edu

• Markov models
• Monte Carlo methods
• Computational statistics
• Markov chain
• Numerical analysis
• Pi
• Dimensionless quantity
• Random walk
• Statistics
• Probability and statistics
• Mathematical analysis

HW  for  Lab  13  (19  Apr  2012)  MCMC  lab.    Due  on  10  May  2012.       1. Make  a  plot  of  these  data  similar  to  figure  6.4

Source URL: www.uvm.edu

The Ising model and Markov chain Monte Carlo Ramesh Sridharan∗ These notes give a short description of the Ising model for images and an introduction to Metropolis-Hastings and Gibbs Markov Chain Monte Carlo (MCMC). Th

Source URL: people.csail.mit.edu

• Computational statistics
• Markov chain Monte Carlo
• Markov models
• Statistical mechanics
• Metropolis–Hastings algorithm
• Gibbs sampling
• Markov random field
• Ising model
• Markov chain
• Statistics
• Probability and statistics
• Monte Carlo methods

ulens mcmc Manual (version 0.1, Jul[removed]Introduction This software aims to help with the fitting of the microlensing model to an observed light curves. It has two components, one is the single lens and other is a binar

Source URL: nexsci.caltech.edu

• Filename
• Data file
• Data
• Information
• Computing
• Computer data
• Configuration file

Efficient Hierarchical MCMC for Policy Search Malcolm Strens [removed] Room G020, A9 Building, QinetiQ, Ively Road, Farnborough, Hampshire, GU14 0LX, UK.

Source URL: www.machinelearning.org

• Applied mathematics
• Computational statistics
• Markov models
• Markov chain Monte Carlo
• Estimation theory
• Particle filter
• Metropolis–Hastings algorithm
• Parallel tempering
• Markov chain
• Statistics
• Monte Carlo methods
• Probability and statistics

The  exponenEal  mechanism  via  MCMC   Aleksandar  Makelov,  mentored  by  prof.  Salil  Vadhan   Harvard  College  ’15,  supported  by  HCRP   Mo#va#on   Mark

Source URL: privacytools.seas.harvard.edu

• Markov processes
• Data privacy
• Monte Carlo methods
• Markov chain Monte Carlo
• Markov chain
• Stochastic process
• Ising model
• Exponential mechanism
• Differential privacy
• Statistics
• Probability and statistics
• Markov models

LESSON 1 AN INTRODUCTION TO MCMC SAMPLING METHODS Recommended Texts Unfortunately it is di¢ cult to recommend a single book that satisfactorily covers all the material in the course. The following two are recommended: N

Source URL: www.statistics.com

• Estimation theory
• Computational statistics
• Bayesian statistics
• Maximum likelihood
• Markov chain Monte Carlo
• Metropolis–Hastings algorithm
• Bayesian inference
• Statistics
• Monte Carlo methods
• Statistical theory

Asymptotically Exact, Embarrassingly Parallel MCMC Willie Neiswanger∗ , Chong Wang† , and Eric Xing‡ arXiv:1311.4780v1 [stat.ML] 19 Nov[removed]Machine Learning Department, Carnegie Mellon University

Source URL: arxiv.org

• Statistical inference
• Econometrics
• Computational statistics
• Estimator
• Fisher information
• Asymptotic theory
• Bias of an estimator
• Normal distribution
• Markov chain Monte Carlo
• Statistics
• Estimation theory
• Statistical theory

CLUSTERING MULTINOMIAL OBSERVATIONS VIA FINITE AND INFINITE MIXTURE MODELS AND MCMC ALGORITHMS Mario Medvedovic, University of Cincinnati Medical Center Mario Medvedovic, Department of Environmental Health, University of

Source URL: eh3.uc.edu

• Cluster analysis
• Mixture model
• Mixture distribution
• Dirichlet process
• K-means clustering
• Multinomial distribution
• Statistical classification
• Probability distribution
• Mixture
• Statistics
• Machine learning
• Statistical models